{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Уровень 0:\n",
    "\n",
    "### Задание 1:\n",
    "\n",
    "Получить интуицию, на что влияют параметры распределений. Построить по 5 функций плотностей с разными параметрами для каждого распределения: нормальное, экспоненциальное, Стьюдента на одном графике."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import scipy.integrate\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Среднее значение для нормального распределения\n",
    "mean = 2\n",
    "\n",
    "sigma_v = (0.2, 0.4, 0.6, 0.8, 1.0)\n",
    "\n",
    "pdf_list = []\n",
    "for sigma in sigma_v:\n",
    "    norm_rv = scipy.stats.norm(loc=mean, scale=sigma)\n",
    "    x = np.linspace(0, 4, 100)\n",
    "    pdf_list.append(norm_rv.pdf(x))\n",
    "\n",
    "#Строим график функции распределения\n",
    "plt.plot(x, pdf_list[0], label=f'$\\sigma = {sigma_v[0]}$')\n",
    "plt.plot(x, pdf_list[1], label=f'$\\sigma = {sigma_v[1]}$')\n",
    "plt.plot(x, pdf_list[2], label=f'$\\sigma = {sigma_v[2]}$')\n",
    "plt.plot(x, pdf_list[3], label=f'$\\sigma = {sigma_v[3]}$')\n",
    "plt.plot(x, pdf_list[4], label=f'$\\sigma = {sigma_v[4]}$')\n",
    "plt.xlabel('x')\n",
    "plt.title(f'Зависимость нормального распределения от $\\sigma$ при постоянном среднем')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf_list = []\n",
    "lam_v = (0.1, 0.3, 0.5, 0.7, 0.9)\n",
    "\n",
    "for lam in lam_v:\n",
    "    x = np.linspace(0, 4, 100)\n",
    "    pdf_list.append(scipy.stats.expon.pdf(x, loc=lam))\n",
    "\n",
    "#Строим график функции распределения\n",
    "plt.plot(x, pdf_list[0], label=f'$\\lambda = {lam_v[0]}$')\n",
    "plt.plot(x, pdf_list[1], label=f'$\\lambda = {lam_v[1]}$')\n",
    "plt.plot(x, pdf_list[2], label=f'$\\lambda = {lam_v[2]}$')\n",
    "plt.plot(x, pdf_list[3], label=f'$\\lambda = {lam_v[3]}$')\n",
    "plt.plot(x, pdf_list[4], label=f'$\\lambda = {lam_v[4]}$')\n",
    "plt.xlabel('x')\n",
    "plt.title(f'Зависимость экспоненциального распределения от $\\lambda$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q87WGRAfJDVnRoTlTo38bmNzQSq31v7TWw7XWw4F7ge+11nU7Fk+wrx/VuqIK0faKLNWk55efaJJxRnJMEAePl1JRXdOGJROi5ZoM9FrrHwBnnwiZDSxsVYmEaEe7s2tvxDanRh9MjU1z4Nipj8IL0RG4rI1eKeWHUfNfXGexBlYqpbYopeY1sf08pdRmpdTm5s5wLoSr1PaeaV6gN9JKO71oSFMTj9RVU1PDiBEjuPjii12Wvytvxv4WWFuv2eZsrfVIYApwi1LqvIY21lq/rrUepbUeFRkZ6cJiCeG8XZlF9Ar2ITzAu+nEdvFhfgT6eLArU9rpxamcmXikrhdeeIGkpCSXlsGVgX4W9ZpttNZZ9n+PA58CY1yYnxAutzuruFm1eTCGnB3cK0hq9F2QuyYeqZWRkcEXX3zBjTfe6Irin+CSsW6UUsHAOOCqOsv8AZPWusT+/0nAo67IT4i2UF5Vwy85pUwZ2qvZ2w6JDuaDjWlYa2x4mKXXskt9eQ8c3enaffYcClMab0Jpj4lH/vznP/P000873GdrNBnolVILgfFAhFIqA3gI8ATQWr9mT3YpsFJrXVZn0x7Ap/YB9j2AD7TWK1xXdCFca+/RYmwakptZowej501FtY2U3DIG9Ahsg9IJd3P3xCOff/45UVFRnH766Xz33XctL7gDTQZ6rfVsJ9K8jdENs+6yFOC0lhZMCHc7cSPWicHM6hty4gnZIgn0rtZEzbutuHvikbVr17J06VKWL19ORUUFxcXFXHXVVbz33nutPhYZplgIu0PHS/H3MhMd7NPsbftE+mM2KX45XtZ0YtEpuHPiEYAnn3ySJ598EoDvvvuOZ555xiVBHmQIBCFOSM0rIyHCv8H5PBvjaTYRG+rL4TwJ9F2FOyceaWtSoxfC7nBuGUNb0GxTKzHCn9RcCfRdhTsnHqlv/PjxjB8/3iV5g9TohQCgusZGRkE5iRH+Ld5HQrg/h3PL2nSmICFaQgK9EEB6voUamyYhvOWBPjHCH0tVDTkllS4smRCtJ4FeCIxmG4DEyNYF+rr7EqKjkEAvBHUCfStr9HX3JURHIYFeCIweN8G+noT6e7V4H9EhvniZTdLzRnQ4EuiFwKiFt+ZGLIDZpIgP95OeN6LDkUAvBJCaa2l1oIdfe94I0ZFIoBfdXkV1DZmF5a3qcVMrMcKPtDwLNpt0sRQdhwR60e2l5VmA1vW4qZUYEUCl1UZ2cUWr9yW6DmcnHiksLGTmzJkMGjSIpKQkfvrpJ5fkL4FedHuu6HFTKyHCz9hnjjTfCENzJh65/fbbmTx5Mvv27WPHjh0um4BEAr3o9moDfW2Qbo0TXSyl502X4M6JR4qLi/nhhx+YO3cuAF5eXoSEhLjkOGSsG9HtpeaWERHgTaCPZ6v31SPQB19Ps/S8caF/bvwn+/L3uXSfg8IGcfeYuxtN4+6JR1JSUoiMjOT6669nx44dnH766bzwwgv4+7f+l6YEetHtHc4rI9EFtXkAk0nRO9xPet50Ae6eeMRqtbJ161Zeeuklxo4dy+23385TTz3FY4891oqjMDgzw9QC4GLguNY62cH68cBnwGH7ok+01o/a100GXgDMwJta6/aZQUCIRhzOLWPCQNdNSN8n0p992a6dCq47a6rm3VbcPfFIbGwssbGxjB07FoCZM2c2euO2OZyp0b8NvAy820iaH7XWF9ddoJQyA/OBC4EMYJNSaqnWuuHpz4Vws9JKKzkllSS4oA99rYRwf1buPibzx3Zy7p54pGfPnsTFxbF//34GDhzIN998w+DBg11yLE1ehVrrH4D8Fux7DHBIa52ita4CFgHTW7AfIdpMqgt73NRKiPDHatNkFJS7bJ/C/dpj4pGXXnqJOXPmMGzYMLZv3859993nkmNxVRv9mUqpHUAWcKfWejcQA6TXSZMBjHVRfkK4hCtGrayvT52eN678pSDcqz0mHhk+fDibN292SZ51ueJ35Vagt9b6NOAlYIl9uaP52Bp8XFApNU8ptVkptTknJ8cFxRKiabU1+t5hrq3Rg/SlFx1HqwO91rpYa11q//9ywFMpFYFRg4+rkzQWo8bf0H5e11qP0lqPiox03Y0xIRqTlm+hR5A3vl5ml+0z3N8Lfy8zR/ItLtunEK3R6kCvlOqp7H2FlFJj7PvMAzYB/ZVSiUopL2AWsLS1+QnhSun5FuLDXNO1spZSirgwPzIKJNCLjsGZ7pULgfFAhFIqA3gI8ATQWr8GzAT+oJSyAuXALG10HLUqpW4FvsLoXrnA3nYvRIeRnm/hjD7Nv8HWlLgwP9Lk6VjRQTQZ6LXWs5tY/zJG90tH65YDjqc5F6KdVdkHH4tzcY0eID7MjzUHc9FaO3w4Rgh3kk6+otvKKixHa9ok0MeF+lJeXUNuaZXL9y1Ec0mgF91W7c3SuFBfl++79ssjXdrpRQcggV50W7VBOD68bZpuwLgHIER7k0Avuq0j+Ra8zCZ6BPq4fN+xoRLoxa+cmXjkueeeY8iQISQnJzN79mwqKlw3eY0EetFtZeSXExvqi8nk+pulvl5mIgO9Sc+XYRC6O2cmHsnMzOTFF19k8+bN7Nq1i5qaGhYtWuSyMkigF91WeoGF2Da4EVsrLtRXHprq5Nw58YjVaqW8vByr1YrFYnE4wmVLyXj0ots6km9hWGxwm+0/PsyPzWkFbbb/7uLoE09Qude1E494Jw2iZxMDhrlz4pGYmBjuvPNO4uPj8fX1ZdKkSUyaNKkVR3gyCfSiWyquqKbQUk1caBvW6MP8WLoji+oaG54yXHGn486JRwoKCvjss884fPgwISEhXH755bz33ntcddVVrTwKgwR60S3V3iRtiz70teJC/bBpyC6saJOePd1FUzXvtuLOiUdWrVpFYmIiteN8XXbZZaxbt04CvRCtUXuT1NXj3NRV+yVyJN8igb4TcufEI/Hx8axfvx6LxYKvry/ffPMNo0aNctmxyO9J0S3VDjjWtk03xoNY8tBU5+TOiUfGjh3LzJkzGTlyJEOHDsVmszFv3jyXHYvU6EW3dCTfQpCPB8F+nm2WR69gXzxMSvrSd1LunnjkkUce4ZFHHnFJfvVJjV50S+n5ljZtnwcwmxQx0sVSdAAS6EW3dCTf0qbNNrXiQv1Il7ljRTuTQC+6Ha2NibvdcYM0LsxPmm5Eu5NAL7qdnJJKKq22Nhm1sr64MF/yy6oorbS2eV5CNEQCveh2jrihD30tGcVSdARNBnql1AKl1HGl1K4G1s9RSv1s/1unlDqtzrpUpdROpdR2pdRmVxZciJaq7e7ojkAfJ6NYig7AmRr928DkRtYfBsZprYcBjwGv11s/QWs9XGvtut7/QrTCkTzj5mhMiDuabn59aEqI9uLMnLE/KKUSGllf91Gx9UBs64slRNtJL7DQI8gbH09zm+cV6udJgLcHGdLzRrQjV7fRzwW+rPNaAyuVUluUUo0+5qWUmqeU2qyU2pyTk+PiYgnxq/R8S5sOfVCXUorYUF9puunmbrjhBqKiokhOTm40nTMTlLSEywK9UmoCRqC/u87is7XWI4EpwC1KqfMa2l5r/brWepTWelTtwD5CtIV0N/WhrxUX5idNN93cddddx4oVKxpN48wEJS3lkkCvlBoGvAlM11rn1S7XWmfZ/z0OfAqMcUV+QrRUldVGdnGFW27E1ooP8yOjoNzhcLWiY3PFxCMA5513HmFhYY2mcXaCkpZo9Vg3Sql44BPgaq31gTrL/QGT1rrE/v9JwKOtzU+I1sgqLEdr9/S4qRUX6kt5dQ25pVVEBnq7Ld+u4sf/HSA3vdSl+4yIC+DcKwY0msZVE484y5kJSlqqyUCvlFoIjAcilFIZwEOAJ4DW+jXg70A48Ip9MH2rvYdND+BT+zIP4AOtdeO/XYRoYye6VrrhYalatV8q6QUWCfSdiKsmHnGWMxOUtJQzvW5mN7H+RuBGB8tTgNNO3UKI9uPOh6VqxdV5aGpkfKjb8u0qmqp5txVXTTziLGcmKGkpGaZYdCvp+eV4mU30CPJxW57y0FTn5KqJR5zlzAQlLSVDIIhuJb3AQkyoL2aTa34SO8PXy0xEgPeJWa1E5+CqiUdq93XmmWeyf/9+YmNj+c9//gP8OvEIND5BSWtJjV50Kxn5FmLd2D5fKy7MV2aa6mRcOfHIwoULHS6vO/EINDxBSWtJjV50K0fcMOGII3Gh0pdetB8J9KLbKK20UmCpdttTsXXFh/mRXVSBtcbm9ryFkEAvuo3am6HufCq2VlyYLzU2TXZRhdvz7qzkATPHWnJeJNCLbuPXrpXt0EYfKqNYNoePjw95eXkS7OvRWpOXl4ePT/N6jcnNWNFt1Nbo26PpJk4mIGmW2NhYMjIykAEOT+Xj40NsbPMGCZZAL7qNjIJyAr09CPb1dHvevYJ9MJuU9LxxkqenJ4mJie1djC5Dmm5Et5GebyE2zM9lj5U3h4fZRHSIj/SlF+1CAr3oNo7kW9w6xk190sVStBcJ9KJb0FqTUVDeLu3ztYzhiiXQC/eTQC+6hdzSKsqra9rlYalacWF+5JZWYamytlsZRPckgV50CyeGJ26HrpW1aodekPljhbtJoBfdQns+LFWr9tfEkTxpvhHuJYFedAu1gT62HQN9fJ0JSIRwJwn0oltIzy8nMtAbXy9zu5Uh3N8LX0+zdLEUbtdkoFdKLVBKHVdK7WpgvVJKvaiUOqSU+lkpNbLOuslKqf32dfe4suBCNEdaflm79rgBY1q4+DA/juSXtWs5RPfjTI3+bWByI+unAP3tf/OAVwGUUmZgvn39YGC2UmpwaworREsdybPQO7x9Az1A73A/0qSNXrhZk4Fea/0DkN9IkunAu9qwHghRSvUCxgCHtNYpWusqYJE9rRBuVVFdQ1ZRBQnh/u1dFBIi/EnLt2CzyWBdwn1c0UYfA6TXeZ1hX9bQcoeUUvOUUpuVUptlICPhSrU3YjtKjb7KauNosQxXLNzHFYHe0cAhupHlDmmtX9daj9Jaj4qMjHRBsYQwpNqbSlpVo7fkQ2mO8Vde0OLd1JYhNU/a6YX7uGL0ygwgrs7rWCAL8GpguRBulWYPqs0O9CVHYddi+PlDyN5x8rqYUTDsSki+DPwjnN5l7a+KtDwLZ/VtXnGEaClXBPqlwK1KqUXAWKBIa52tlMoB+iulEoFMYBbwOxfkJ0SzpOaVEeLnSbCfk8MTWyth9WPw03zQNug1HM7/O3gHGevLC2HPEvjyLvjqXjjnDhj3NzA3vf9ewb54mU1Soxdu1WSgV0otBMYDEUqpDOAhwBNAa/0asByYChwCLMD19nVWpdStwFeAGVigtd7dBscgRKPS8iz0drZr5fG9sPgmOLYTRl4DZ94KkQNPTTfuLji6C9a9CD88Db+shsteh/DGq+lmkyIuzJe0XOl5I9ynyUCvtZ7dxHoN3NLAuuUYXwRCtJvUvDJGxIU2nXDXYljyR/AKgNkfwsDGehUDPZON4D5gMnz+F3jtXJj5Hxg4pdHNEsL9pUYv3EqejBVdWpXVRmZBOQlN9bjZvcSoyUePhD/+1HSQryv5MvjDOogcAB9eDQdWNpq8d7g/R/ItMh+qcBsJ9KJLyyiwYNNGcG3Qvi9g8VyIHQ1zPoKAqOZnFBwDV38KPQbDh1cZTTkNSIjww1JVQ05pZfPzEaIFJNCLLq32KdSEiAZq9L+shv9da9xwnfMReAe0PDPfULh6CUT0h4WzIe0nh8lqv3TkCVnhLhLoRZdW2xbusEZfeAQ+ut642XrVYvAJan2GfmFwzWcQHAv/u8boollP7Y3h1FxppxfuIYFedGlpeRYCvD0I9/c6eYW10gjEWsOV/wXfENdl6h8BV/wXKkvg47lQc/KMUjGhvphNSmr0wm0k0IsuLS2vjN7hfihV70Htr+6DrG1wySsQ1sf1GfcYDL99HtLWwLf/OGmVp9lEbKiv9LwRbiOBXnRpaXmWU5+I3fkxbHoTzroNki5uu8xPmwWnXwdrnoP9K05a1TvcX2r0wm0k0Isuy1pjI73AQnzdrpUlx+CLv0LsGDj/obYvxOR/Qo+hsOxPJ42RkxDuR2pemXSxFG4hgV50WdlFFVTX6JP70H95F1SXG002TgxZ0GqePizdN+QAACAASURBVDD9ZSjLhZUPnFjcO9yfkgorhZbqti+D6PYk0Isu65QeN3uWwp7PYPzdRhdId4keDmf/Cba9d6J/fe2Xj7TTC3eQQC+6rJOGJy4vMJpseg6Ds/7k/sKMuwfC+8Gy26GyVPrSC7eSQC+6rLTcMnw8TUQFesPKB8GSZzSjuKPJpj5PH5j2MhSmw7dPEBfmi1JSoxfuIYFedFmpeRZ6h/ljyt4K2/4LZ/4Rep3WfgXqfabRC2fDa3jnHyQ62FcemhJuIYFedFkpuaUkhvvAl3eDfxSc97f2LhJMfNAYZmHFPSSG+3FYAr1wAwn0okuqstpIy7MwzbQWMjbBBQ+7ZoiD1vIPh/H3Qcq3/MZ7G4eOl0oXS9HmJNCLLik1rwxvWzkT0ucbQw+f1ui0Cu41ei5EDuLi7Jeprqogu0gmChdty6lAr5SarJTar5Q6pJS6x8H6u5RS2+1/u5RSNUqpMPu6VKXUTvu6za4+ACEcOXS8lFs8luBbmQNTngZTB6rTmD1h8pMElmcw1/wlh46XtneJRBfX5NWvlDID84EpwGBgtlJqcN00Wut/aa2Ha62HA/cC32ut8+skmWBfP8qFZReiQUfTD3Gj+UusQ2ZC3Oj2Ls6p+k6ksu9k/ujxGekZ6e1dGtHFOVPNGQMc0lqnaK2rgEXA9EbSzwYWuqJwQrTU4H3zUUrjcaEbhjloIa+LHsFPVZCw55X2Loro4pwJ9DFA3SpHhn3ZKZRSfsBkYHGdxRpYqZTaopSa11AmSql5SqnNSqnNOTk5ThRLiAYc28Po4q9YHTgdQuLbuzQNUlGD+Nb3IsbmfgoFqe1dHNGFORPolYNlDXUT+C2wtl6zzdla65EYTT+3KKXOc7Sh1vp1rfUorfWoyMhIJ4olhGN61cOUaR92972pvYvSpE0J86jGBKv/0XRiIVrImUCfAcTVeR0LZDWQdhb1mm201ln2f48Dn2I0BQnRNlLXog5+xavWacREO/zh2aFERieywDoZdn4E2Tvauziii3Im0G8C+iulEpVSXhjBfGn9REqpYGAc8FmdZf5KqcDa/wOTgF2uKLgQp9AaVj1EhW8PFtRMpn+PVsz/6ib9ogJ4zToNq3cIrHq4vYsjuqgmA73W2grcCnwF7AX+p7XerZS6WSl1c52klwIrtdZ1H/XrAaxRSu0ANgJfaK1PnoFBCFc5sAIyNrEh/iYq8aJfZGB7l6hJ/aICKMGPHYlzjZEtU9e0d5FEF+ThTCKt9XJgeb1lr9V7/Tbwdr1lKUA7Di4iug2bzWjnDuvDcvNEIgIKCPZrh8HLmik62BdfTzMrfC/m9MAP4JvH4IYVUH/qQyFaoQM9RSJEK+z5FI7tgvH3cSC3gn5R/k1v0wGYTIp+UQHsy62G8+6C9PVwaFV7F0t0MRLoRedXY4Vvn4Cowejkyzh0vJT+UR2/2aZWv6gAfjleCiOuhpDesPox436DEC4igV50fj8vgrxDMOF+ckqrKamw0i+q49+IrdUvKoCsogpKa0ww/l6j983eU/o7CNFiEuhF52athO/+aQxcNug3HLSPG+PKQK+1xlJtobSqlNKqUizVFpeOONk30ijrL8dLYdgVEDEQVj8OthqX5SG6N6duxgrRYW19F4qOwG+fA6VODBDWkkCvtSajJIOduTvZmbuTAwUHOGY5xrGyY1TUnDzCpK+HLz38etDDvweDQgeRHJnM0IihxAQ0v+9+bVkPHS/ltLgQmHAffHQt7PwYTruy2fsToj4J9KLzqi6HH5+F+DOh7/mAESwDfTyM6QOdYNM2th/fzuojq1mdvpr0EmO0Dx+zDwPCBpAUlsS42HGE+4ZjVmYAanQNueW5HLccJ6s0i4X7FlK1pwqAhKAEJsZPZGL8RIZGDMWkmv7R3DvcD0+z4lCOfRTLpGnQYyh8/xQkX9Y+Ux+KLkUCvei8Ni+AkmyY8eaJ7oiHjpfSLyoA1UT3xLzyPD499CkfH/iYzNJMPE2ejO01lmsGX8PwqOH0C+mHh8m5j0d1TTUHCg+w/fh2vk//nnd3v8uCXQvoHdSbywdczvS+0wnxCWlwe0+ziYRwfw4eswd6kwkm3g8LZ8GOhTDyGufOhxANkEAvOqfKUljzHCSOg4RzAKPpZd/RYiYN7tngZunF6by+83U+T/kcq83KmJ5juG3EbYyLHUeAV8va9T3NngwJH8KQ8CHMSZpDcVUx36V/x8cHPuaZzc/w4tYXmd5vOjcOvZHogGiH+xjQI5CfMwvrLJgMMafD90/DsCvBw7lfKEI4IoFedE4bX4eyHJj4wIlFWUUVFFiqSY45dcrAjJIM/v3zv1n2yzI8TB5cPuByZg2cRZ+QPi4vWpBXENP6TmNa32nsz9/Pov2L+PTQp3x66FMu7Xcp84bNo6f/yV9GQ2KC+GJnNkWWauNBL6Vgwv3w3mXGfYgxHX+ANtFxSaAXnU9FEax7EfpPgrhfx8jbnVkEwJCY4BPLLNUW3tj5Bu/sfgeTMjF70GxuSL6BSD/3jJA6MGwgD535EL8f9nve3Pkmiw8uZtkvy7hh6A1cP+R6fDx8AEiONsq8O7uIs/pGGBv3nWjcf/jxWRhxFXj6uqXMouuR7pWi8/npFSgvMGq8dezKKsakIKlnEFprvkj5gos/vZg3d77J5ITJfHHpF9w95m63Bfm6evr35IEzHuCLS7/gvNjzeGX7K0xbMo1VacZTsEOijV8huzOLf91IKeMXS0k2bPqP28ssug4J9KJzseTDT/ONninRw09atTuziH5RARRV53DLN7dwz4/3EOUXxXtT3+OJc5+gh3+Pdir0r6IDonl2/LMsuGgBgV6B/OW7v3DHd3egzSVEB/uwK6vo5A0SzoE+E2DN/0FlSfsUWnR6EuhF57LmOagqPaU2D7Azq5CQqK1c8tklbDq6ibtG3cX7U9/ntMiON67e6J6jWXTxIm4feTvfp3/P9CXTiYrezc66N2RrTXwQLHmw/rVT1wnhBAn0ovMoOQob3zCeHo0adNKqg7lHKQ56k73WBQwJH8In0z/hmiHXYDaZ26mwTfM0eXLj0Bv5aNpHJAYn8gtvkOX1H46WFJycMPZ0GPgbWPeS0WQlRDNJoBedx4/Pgq0axt9z0uKfsn7iupWz8PDfz+WJf+SNSW8QFxjXwE46nj7BfXhn8jtMibkOj8CdzPricrYe23pyoon3Q2WxEeyFaCYJ9KJzKEiDzW/B8DkQZnSJrLHV8Mr2V/j917/HrH2xpN7Cn8fc6NTTqB2N2WTmjtG3YEm9GatNccNXN7Bg14Jfx9TpMcR4Snb9q1B6vH0LKzqdzveJEN3Tt0+AyQzj7gYgvyKfP6z6A6/ueJXf9v0tg2wPEh/QjyCfzjtcQI8gb8I8+jHa41Emxk/kuS3P8adv/0Rxlb0nzoT7oabKeIhKiGZwKtArpSYrpfYrpQ4ppe5xsH68UqpIKbXd/vd3Z7cVoklHd8HPH8KYeRAcw67cXVyx7Aq2HNvCw2c+zD/O/gd7sytO6j/fGSmlGBIdzL5sK8+Oe5Z7xtzDmow1XLnsSg4UHIDwvsZwCFvegvyU9i6u6ESaDPRKKTMwH5gCDAZmK6UGO0j6o9Z6uP3v0WZuK0TDVj8GPkFwzl9Y+stSrv3yWszKzHtT32PGgBkUl1tJzy8/8dBRZ5YcE8TBYyVUWm3MSZrDW5PforKmkquWX8XK1JXGLxqTp/ELRwgnOVOjHwMc0lqnaK2rgEXAdCf335pthYC0n+DACqxn/Yl/7nqD+9fcz/Co4Sy6eBFJ4UkA7Lb3PXc09EFnkxwdjNWmOXDM6DM/PGo4H178IQNCB/DX7//Kiwf/h23szbDzI8j+uZ1LKzoLZ4ZAiAHS67zOAMY6SHemUmoHkAXcqbXe3YxtUUrNA+YBxMfHO1Es0eVpDaseojioF3eV72Pd4Q1clXQVfx3115NGlqx9yGhIK2v02mrFevw41txcrLl51BQUYLNYjL+K8hPplFIoH19Mvr6Y/Pwwh4XiERFh/EVGojxaPrJI7THsyixmWKwx4mWkXyQLLlrAExue4I2db/BLzLk86RuC3zePwFWLW3XMontw5op0NN5r/el1tgK9tdalSqmpwBKgv5PbGgu1fh14HWDUqFEyYaaAfZ9zJHsLt/ZNJv3YFh456xEu63/ZKcl2ZxUTHexDmL+XU7u1lZdTsW8flQcOUnngAFWHU6hKz6A6KwtqGpjVSakTQyFjszW8cw8PPKOj8YqNxatvX7wH9Mdn4EC8BwzA5OPTZNniwnwJ9PE48SullpfZi4fOfIj+of15etPTXBOfwEuHvqVXynfQZ7xTxy26L2cCfQZQt1NyLEat/QStdXGd/y9XSr2ilIpwZlshHLJWsWn1g/wlNhqo4fVJrzO652iHSXdmFjV6I7b62HEsGzdg2bqV8h07qNx/4ERAN/n54dW3L77DhhH0m6l4xsScqJ2bQ8Mw+fth8vNDeXmdGONea42uqjJq+mVl1OTnY83Nw5qbQ3VGJtUZ6VSlHaHw44/R5fZfAh4e+AwahO+wYfiNOh2/MWPwiIg4paxKKZKjg9mVWeRw3ZykOfQO6s1d39/J7NgYXvr6HobetNbokSREA5wJ9JuA/kqpRCATmAX8rm4CpVRP4JjWWiulxmC0/ecBhU1tK4QjS7+5i4f8rcT69WD+5LeID3LcnJdfVkVKThkzRsaeWGarrMSycSOl331P2U8/UZVi9FAx+fvje9owAubdhG9yMt4DB+IZHY0yNa+XsVIK5e2NydsbQkMhNtZhOm2zUZ2eTsX+/VTs2k35jh0ULllCwQcfAODdvx/+Z51FwLhx+I0ahfIyfpEMjw/hjR9SsFRZ8fM69SN6Tsw5vDf1fW758jquJ58nv3+ICyf8o1nHILqXJgO91tqqlLoV+AowAwu01ruVUjfb178GzAT+oJSyAuXALG086eFw2zY6FtEFaK2Zv+kZ/n10NWOUL/837ROCfRqurW88nA/A2EhPij77jOKVX1O2bh26vBzl44PfmNGEzJiB3xlj8Rk0CGV2X81XmUx49e6NV+/eBE2aBBj3ASr27qVs/Xos6zdQsHAR+e+8i8nPD/9zziFw0iTOiBnMqzbNtiOFnN3v1Fo/QN+Qvrw//VNu/3ASdxz5jD9vj+GG025ucmYt0T0pV85m7yqjRo3Smzdvbu9iCDerqqniwbUPsvzwci4tKePBGZ/i2avhAclsFgvvPPMuHqtXMir3IFitePTsSeDEiQSMH4ffmDFOtYu3J1t5OWXr11P63feUrP6Gmpxc8PRkffgAvCdPYdaf5zR6DBWpa3jwi2tYEeDPjP4zuP+M+/E0dd6HxkTLKaW2aK1HOVwngV50BIUVhdz+7e1sPb6V2wuKmZv4W9T0l09Jp202LJs2U/TJYoq/XoW2WCgKDCNx5nSCLpqEz7BhzW6K6Si0zUb5tm0Uf/UVaR8vJchShMnfn8DJFxEyYwa+I0Y4rLHb/nctLx/7kTeC/Dk7+myeGfdMi6dFFJ2XBHrRoaUXp/PHb/5IVmkW/9BhTMnYC7dtgcBfx4+35uRQuPgTChcvpjo9HVNAAD6TLuLWnCjOv3wSf540qJEcOp/Hl+1k29LVPBd2FMvXK7FZLHglJhIycwbBl16KR1jYr4kL0mD+GD7pO5rHqjNJDEnklfNfOWW6QtG1NRboO2fVR3QZ249vZ87yORRUFvDGwOuZkrIRJtwLgT3QWlO2fj0Zt/+ZgxMmkvP883j26kX00/+k/48/cOjq29gZ3pexfd0/Y1RbG9M3is3h/cj6/Z30//EHej3+OObQUI7/6xkOjRtP5p13YdmyxRj0LLQ3nPMXLtv/I/OH/J7s0mzmfDGHvXl72/swRAchgV60m69Sv2LuV3MJ9Ark/Qv/w8i1/4bIJGzJV1Gw6EMOT5vGkeuux7J+PWFXX02fL5fT+913CJ42DZOvLxsO5+NlNjEiPqS9D8XlxiSEoRRsOJyPyd+fkBmXkfDB+/T5fBkhV15J6XffkTbnKg5fNoPCxZ9gG3UzhPTmrJ8W8O5FCzCZTFy74lp+yPihvQ9FdADSdCPcTmvNW7vf4rktzzE8cjgvTnyR0J9epXrFvyjwvo6Cr37CVlSEd1ISYVddRdBvpjq8ITl9/lq8zIqPbj6rReWw1dgoL62mvKSayrJqKi1WKsurqa6sOfFns2pqamzYak7+nJjMCrOHCbOHCQ8vE57eZjy9zXj7eeLt54GPvye+gV74BHhiMrWsJ8yUF34k3N+L92489WFym8VC0bLPKXjvv1QePIQ5NJTQSaMJrXwXj2mPcnz4ldz6za3sL9jPvWPuZdagWS0qg+g8Gmu6afmz2kK0QLWtmsfXP87ig4u5KOEiHj/ncfTWtWQ9/QZFR3qB7SsCL7iAsGuvwff00xvsLlhaaWVXZhF/GNe3wbyqyq0U5ZRTlFNOcW45JXkVlBZUUFJQiaWokvLS6gae0zYoBWYPEyYPkxGsa4uiwWbT2Kw2aqw2GqsrKQU+AZ74h3gTEOpDQKg3geE+BEf6EhThS3CkL14+jj+GYxPD+HBTOtU1NjzNJ//4Nvn5EXrlFYRccTmWDRvIf/sdcj9cSZ65F8G7nifsyTG8Pflt/vbD33h8w+OkFadx56g7O/SMW6LtSKAXblNcVcxfv/sr67PXc1PyjdxgPYNjf/gTZT/8gPLwJnTGJYTdeDNeTox1tDWtgBqbZkxiGJXlVvIyS8nPLCUvq4yC7DIKjlmwFFWdtI23n4cRbMO86ZEYhF+QF36BXkbN298Db39PvH098PLxwNPbjMlDNdkvXWtNjdVm/AKoqKHSYqXCUk1FqfFnKa7CUlxFWWElJXnlZB0spKrcetI+/EO8CenhR1gvf8Ki/QmPCSA82p+xiWG8vS6VnZlFjIwPdZi/Ugr/M87A/4wzqExJIf/1lylatpzCy68j4PzzeWru9bwcGMt7e98jozSDf577T/w8/Zo8v6JrkaYb4RbpJenc+s2tpBel8U/zFfRf9jPlO3ZgDvIjrPdRQv94L+YJtza5n4qyao6nFbN4VQppBwsZEeRPaV7FifVePmZCe/kT2sOPkJ5+BEf6GbXnSF+8fTtGvaairJqSvAqKcsopPG6h8KiFgmMWCrLLqK78daydgAgfNheVkpQUwW8nJhDVO8ipY7Auf5L8N+dTcCQKW2k5fqNG8fPUATxQ/REDwgby0sSXpEdOFyTdK0W72nJsC3esup3Td1dyw7YQzIcz8IyJIWzOTEKynsAUkwzXfg71+r9rmyY/u4zsX4o4mmL8FR3/dRRJi5diaHIkkfEBRMQGEhbtT0Cod6d9OlRrTUl+BfmZZeRmlJKbXsKOXTkEVP+aJqSHHz37BtOrTzA9+wQT2tMPVf8eQI0V3pyILS+bwsg7yHvvQ6zHjmHtH8+/T8tlb3IQL1zwEskRye49QNGmJNCLdrNk78d8/8YjXLZeEZFfjXf/foTfdBNBU6eiFl8PB76CP6yFiP7YamzkHCkl80AB2YcKyf6liEqL0czhG+hJzz7B9EgMIjDaj8sWbmbOeYncOyWpnY+wbT2wZCfLN2fy0ZWnU5BeyrHDxRxNKaaizIj+Pv6e9OoXTK9+IcQMCCEiLtC4n5C9A16fAKfNRk99jqJly8h7402qUlM5FunBp2eamPL7J5jc9zftfITCVeRmrHC7KkspS56/lV6fbmBeCXgOSaLHo38kYOJE48nV3UvQe5aSf/rjpG/3JmP/DrIPFlJVYTRdhPTwo++ISHr1D6FX32CCInxP1NSX7siiVGsuSOrRWBG6hPOTevDe+iMc8dRMmJoIGDX/ouPlZP9SSNahIrIPFnJ4Ry5g3Ifo1S+EuKQwYpPvJnTbE6jkSwmZMYPgSy6hZOVKPF6Zz81Lf+HYD3fyyczPmXbbc3h4d+yhIkTrSI1euJStrIzs998h+43X8C+pJm9AFEPvfJSgc89DKUVZUSUZW1M48vli0iuHUW41HtUP6eFHzIAQYgaGEjMgFL+ghseWv+WDrWxIyWPDfRdgbmHXxc6i0lrD6Y+t4uJhvXhqxrAG05UVVpJ5oIDM/QVk7C+gONe4b+HvUUSc7x7ipl1J3PB4fAO90FpT+M3X7Hr2ESIO51Ma4k3czbcRNavxcXVExyZNN6LN1ZSUUPD+++S8tQCKStiZaCLwpuuZNP0OjqUUk7Y7jyO788hNLwXA11REbHIv4ob3JnZQKIFhzgWYiuoaTn/sa6YNj+HJy4a25SF1GH9auI21h3LZeL/zX2zFueVk7C8gfWsK6XvyqNSBoCAqPpD4IeHEDwknsncAX/zvcWreWkRSuoawUKLm3kjorCsx+fu38VEJV5NAL9pMTWEh+e++S/5/38NWUsL2/mZWn9uLmcMfg7QAjuzJp6rcijIpevUNJj7wIPFpTxFxyW2o0Tc0O79v9h5j7jubefv60YwfGNUGR9TxfPFzNrd8sJUP553B2D7hzd7e9uML5Kx4lyOJD3IkP45jh4vQGrz9PYgfHE51bD5LNv+di9bkMuSwFXNICGHXXUvonDmYAwPb4IhEW5A2euFy1txc8t95h4L3P6DGYiFtzEhWREfSU41l+OEeHEqx4Btkpc+ISBKSw4lNCsO7eD+8fhsMmQCjrm9Rvit2HSXQ24Oz+joep70rGj8wEi8PEyt2H21RoDedfSs9fvmaHlm3MPr3P1DhM4z0vfmk7TJ+ZZVvqmaE6UH2j8hizbCN/CYjl5rnXyDvPwsIu/oqQq++Go9Qx/34RecgNXrRLNXZ2eT9ZwH5Hy+mwK83BSMmc8gjCs9KY3KQiPgAEodGkDAsgsi4wF+7/lUUwxsTjH//sA4Cmj8QmbXGxujHV3HegEhemDXClYfV4d34zib2Zpew5u4JLes+WpgOr50DwXEwdyV4GQ9N2Wya46nFpO7M5fDPOeRnWgCw+hTSv+IYIduWE1J9lPBZVxB23bV4RnWPX1GdUatr9EqpycALGLNEvam1fqre+jnA3faXpcAftNY77OtSgRKgBrA2VBDRsVUePszRN94idd1hcsKGkn/mk1RrT6y6mqP+hxg+zpeLJ0wgINT71I21hiV/gPzDcO2yFgV5gE2pBRRYqpk8pPs97HPRkJ6s2nuc3VnFJDcyP26DQuLgsjfggyvgizvgkldBKUwmRU97n/wzpvelOK+cz775hj1bj2Gq7IdpxF/wUlWErdtG5PI7SBw3kB43XY9XA9Mnio6pyUCvlDID84ELMSb73qSUWqq13lMn2WFgnNa6QCk1BXgdqDsS0wStda4Lyy3cJG/jz+z972oyjprJDz0HPWg83n4mKmNz+Vp9ikdcJc9c8DR9Qvo0vJO1L8C+z2HS45BwdovL8tXuo3h7mBg3sOsNS9yUC5J6YDYpVuw62rJADzBgEoy7G75/CmJHw+i5pyQJCvfl6isuZt8F+7hr1d2YM4K5kEvJ9z6ToxVj2Z1VRdhfFhMXA0nXTSJ0eNd+jqGrcKZGPwY4pLVOAVBKLQKmAycCvdZ6XZ306wH5uu/E8rNLOfD5NlI2ZVNgigCG4d+zkqFjehI4zIen0x9hZ97PzBwwk7+N/hu+Hr4N7yzle/jmERg8Hc68pcVl0lqzcvdRzu0f6XDC7K4u1N+LMQlhrNh9lL9OGtDyp3/H3Q2Zm+HLu6HnMIgb7TDZoLBBLLzkff6x4R+8lvJ3Rg0axV9iHqBgawkpmzXbKrzZ9momYepn+o6NY8BvhhMSJWPodFTOfGJigPQ6rzM4ubZe31zgyzqvNbBSKaWBf2utX3e0kVJqHjAPIN6JQa2E62ib5lhqMSnbjvPLT2kUlxpDEQRWVpLcJ4/BvzuP8L6RLPllCXdu/Cdmk5lnxz3LpIRJje/4+D748GqIGADT5xtDObbQmkO5ZBVV8LfJXWsmqeb4zbBePLBkF9vTCxnRwCBnTTKZjCacNybAwllw4yoIS3SYNMArgKfOfYqzo8/msfWPcXPR1Tww7gGuu24Kx/ZksffDNRxJs7JpQwWbNqwnOEjT76ze9BkRRWR8YKcdiqIravJmrFLqcuAirfWN9tdXA2O01rc5SDsBeAU4R2udZ18WrbXOUkpFAV8Dt2mtG50NQW7Gtj1rdQ0Z+wo4vCOX1B05WEqqUbqGkIID9NSZDLh4JNFXXozJx4fc8lweWfcI32V8x5ieY3js7MeIDohuPIOSY/DmBWCtMIJJaO9WlffGdzaxPb2QtfdMxNujew61W1pp5YwnvuGCpCieb+3N6NyDxvvjH2ncnPULazT5keIj3LfmPnbk7GBywmQeOOMBgr2DsVksZHywlINf7uCoR28KQ/qBMuEf7EXiaZEkDo8gZkAoZg+Z46ittaofvVLqTOBhrfVF9tf3Amitn6yXbhjwKTBFa32ggX09DJRqrZ9pLE8J9G2jorSatF25HN6RS9qefKyVNXioGsLydhNxdAuxsR70uP53BJ5/PspkQmvN8sPLeWrjU1iqLfz59D8zJ2kOJtXEh7aqDN6aCrkH4LovIGZkq8qdnm/hvH99y60T+vHXSQNbta/O7uGlu3l/Qxrr7jmfyEAHN76bI20dvDvdaK+/+lPwaHx/VpuVt3a9xSvbXyHEJ4T7x97PBb0vAEBbrZSsXEn22wvJPGYir9co8kKTqNEmvHzMxCeHkzgsgvgh4fj4e7au3MKh1gZ6D+AAcD6QCWwCfqe13l0nTTywGrimbnu9UsofMGmtS+z//xp4VGu9orE8JdC7TsHRMlJ/ziN1Zy7ZhwrRGvz8FFEVKQTv+JLQokOETJpI2HXX4Tvs10fsj5Yd5R/r/8H3Gd8zLGIYj579KH1DGp7k44TqClj0O0j5FmZ9AAOntPoYnli+l/+sOcyauyfQK7iR+wHdQEpOKROf/Z47LhzAn87v3/od7vwYFs+FQRfD5W+DuekgvC9/Hw+ufZB9+fu41X+ibwAAIABJREFUsPeF3Df2PiJ8jecatNaUb91K3ltvUfTtjxSEJ1E0bDLHPHtTUa4xmRS9+geTOCyShGHhBEdKu76rtPrJWKXUVOB5jO6VC7TWjyulbgbQWr+mlHoTmAGk2Texaq1HKaX6YNTywbgf8IHW+vGm8pNA33I1NTayDxWRujOX1J9zTwzrG97Lj57euQRvXor3rjWYg4MIveJyQmfPxjP612YYq83Kon2LeHn7y9TYarhtxG3MSZrj3MxE1kqjTf7gVzDtJRh5TauPp7yqhjOe/IZz+kUwf07rfhl0Fdcs2Mj+o8WsuXviKTNPtcj6V2HFPTDkUrjsTTA3feuu2lbN27ve5tUdr+Lr4cvtI29n5oCZJ/3aq0pPp+C99ylcvJia0rL/b++8o+yozgT/uy/n1zmqo0IrtQJCCEsII7BAyGJIDtj4YMYzeLxebHNm56yZsddnd8w6jMOa8XrHMA5jGxs8YDIIIxNlsIRAKHa31Oqgzq/zy/nd/aOeWqG71a9bHUTr/s65p+q9ulX11ffqfXXru9/9LpE11+Jdu53ucDZDPdp9mV1ko3JVHpW1eRRVu9BNx/VcoqgUCPOckC9G29EBWg8P0F6vpRzQGQQLlmSzoCiFq+4VEi88Tsrvx7x0Kdmf/hTum25CZz27dXyw7yAP7HmAhsEGNpVs4msbvkaZqywzIZJx+M/PwrEX4KM/HDN0byo89k4b9z95eMrD/+cjp9JA/N9Pr2XHqgn6SjLlrX+FXf8Daj8Otz4EGU452Oxt5oE9D7CvZx+1ebV8/cqvszx3+Vl1koEg3meeZuh3jxJrakKflYV+xx0M13yYjk5JV+MwqaTEbDNQviKXipW5lK/IweoYP7GdYjTK0M8zTo1mPHl0gLYjA/S2+UGCzW2iYmUuFTUuXG37CD71BOH33kMYjTivv57sOz+Nde3aUdEQnqCHB/c/yHPNz1FgK+Cr67/K1oqtmUdNxMPwxOfg2Itw4/dgw+en5TqllNz44G4Adn5ls4riSJNMSbZ8/3UKXeYpT4w+Jrt/AK/8M6z8mDagypCZoZVS8nzz83z/3e8zFBnitsW3ce/ae0fcOWfWC+19h6FHH8X/yiuQSGDbsAHbLR9nuGQNbfVeTh4dIOyPg4DCSlfa6OdSUO4cPcGK4iyUoZ8HBIaitNcP0HZ0kPaGQaLBBEJAYZWbipU5lK/MxTHUgvepp/A9/wKpQABjRTnZn/gE7ltvxZAzOqoiFA/xq7pf8csjvySRSnDX8ru4Z9U92I2TyFwYGoRHPwXte2H79+CKe6btmnfVebjn1+/yndtqueMKFXJ7Jj/b3cwDL9Tzu3s2TG/en90/1MY9VF8Dn3wEzJknNfNGvTx06CEerX8Us8HMPbX3cOeyO7EYRmcmTfT1MfyHJxl+/HHinZ3o3G7cN92E+9Zb8NoW0HZkgJNHB+k96QOpTbBetiyH8hU5lC3Lwe6+wI7oeYgy9B9AYpEEXY3DdNQP0VY/yFB3EACby0T58hzKV+RStjwHva8f73PP4336aWLNzQizGde2G3Dffju29evHbAXHk3GeaHyChw4+xEBkgK0VW/n7dX/PAuckx7l5O+CR22GwGW57WPPxThPRRJLr/8+bGPU6dn5l8/T4oucRkXiS637wBk6Lgee/dBWG6dTP+7+FZ78ERSvhzifAMbn8Ni3eFn7w7g94o+MNCmwFfHH1F7l50c0YdKN9/zKVIrRnD8NP/AH/rl3IeBzzkiW4b7kF146PkrBm0VY3SFvdAO11g1prH8gtdVC2PIeypdkUL87CaLo0Q27PRBn6DwDJeApPq5eOY8N0NAziafaRSkn0Rh0li9wsWJZD+fIccksdpLxefC+/jO+55wm9+y5IiXXdOty33Ixr27ZxU8vGk3Gea36Ohw89TGegk8sLL+e+dfexOn/15AVufQsev1uLk7/jd1C1+cIUcA4PvdHEt3c28KvPXcGHl1x6KQ8y4VT64gduWclnrrywcQqjOP4yPP5ZsObAJ38NpesmfYh9Pfv40f4fcajvEJWuSj6/6vPcWHXjmAYftJTXvp07GX76aSIHD4FOh/3KDbh23IRz60fQ2R30tftprx+kvX6Q7iYvqYREZxAUV7tZsDSb0pocCiqd6C/BhoEy9BchyUSK3lYfnceH6WocovuEl0Q8BQLyy5yULctmwdIcihe6MZj0JH0+/K+8iu+lnQTfehsSCUxVVbhu2oF7xw5M5xlNHE1GeebEM/z88M/pCnaxPHc5X177ZTaWbJy831tKLUrj5a9rIyo/+QgUTG++kz5/lC3ff50NVTn8/O6xh+grNJ/3Jx/eQ6PHz+v/sAW3bZrj07sOaFFUgR7NLbfu7inJ+Fr7a/zkwE84PnScMmcZ99Tew47qHRjPE8oZbW7B9/xzeJ97nnh7O8JoxL55M64bt+HYsgW9w0E8mqT7xDDtDUN0NAyOTGpjMOspWeSmZHEWpUuyya+4NAy/MvQXAbFIAk+Lj64Tw3SfGMbT7NMMO9praMmSLBbUZFOyOGtkQEmivx//K6/i37WL4J49kEhgLCnBeeM2XNtuxLJyxXkN9VBkiN8f+z2PNjzKYGSQVfmr+MKqL3BV6VVT69gMDWqZD48+BTUfhVv/DSxTTLB1Hr76xCGefL+DP953NdX5jmk//nziaJeXHT/+M3+9sYpv3LR84h0mS2hQi7NvehVWfxpu/C5YXJM+TEqmeK39NR46+BD1g/UUWAu4c/mdfGzJx3CZxj+elJLIwYP4du7E99IfSXg8mtHfuBHn9dfjuHbLSK78cCBG1/FhOo4N0dU4zGCX5u40mHQUVWuGv3hRFoWVLozm+efqUYZ+lpFS4h+I0NPipafJR3fTMAMdAaTU0r3klTkpWZRFyWKtWBzGkf2ixxsJvP46gVdfJXzoEEiJsbwc59aP4Nq6Fcvq1RMa6YbBBh5reIwXml8gkoxwVelV3L3ibq4oumLqkSvH/6j5bUMDsOVrsOk+LW/KNPOXpgE+/bM93LO5mn/arjIjZsI/PnmYx99t54n/spE1ZVnTf4JUEl7/Duz+PrhKtbxF1R+e0qGklLzV9Rb/cfQ/2Nu9F5vBxk0Lb+KOmjtYlL3o/PumUoTffx//y7vw79pFvKsLdDqsa9fivHYLjmuuwVRdPXKPh3wxuhqH6ToxTFfjMAOdAZCg0wnyyhwULXSPpGjOdCrLixll6GeYaDhB30kfnlYfnhathHwxQGtNFFa5KV6olaJqNybraR9l0u8ntHcvgTd3E3jzTRI9PQBYamu1m/faazEvmThbYSgeYtfJXTxx/AkO9B3AorewvXo7n1n2GRZnX8AIykAf/Ol/woFHoGAF3PpTKB5/kuoLoXM4zE0//jPZNiPP3HsVDvOll6VyKgyHYuz48Z9JJCXPfmkTBc4ZMlod78JTfwcDJ+Dyv4Frvz5hjpzzUT9Qz2/qfsNLrS8RT8VZX7Se2xffznXl140ZqXMmUkoidXUEXnkV/2uvEa2vB8BYWorjw1djv2oztiuuQO84HUEWDcXpbvLS0+Slu8mLp9VHMv1Wbc8yU1jlorBSK/kVTkyWD9b9pwz9NBILJ+hr99PXppXek36GPaGR7e4CK4VVLoqqNKOeW2o/a7RfKhYjfOAAob3vEHz7ba3Vnkyis9uxb9yI/erNOK6+GmNh4YSypGSK/Z79PN/8PDtbdhJKhKhwVfCJJZ/g5kU34zZfgFslmYB9P4PXvgXxIGz8Mlxz/4T5UKZKJJ7k4z/9Cy39QZ65dxMLlctmUhzt8nL7v71Nbamb3/7tlZhmKolYLASvfhP2/hQsWXDdN7QR0BkOsBqLwcggTzU+xePHH6cz0InT6GR79XZ2VO9gdf7Eb7AA8a4uAm++SeDN3QT/8hdkOAwGA9Y1q7X/1YYNWGtrEabTYwOSyRQDHQF6mr30NPvwtHjx9Ue0jQKyC20UVLjIL3eSX+4kr8xxURt/ZeingJSSwFCUgY4A/R0B+jv89LUH8PWFR+rY3SYKKl0UVLgoqHBSUOkalbApFQ4TPniQ0L53Cb33HuH330dGo6DTYVm5EvumjTg2bsS6evVZN+F4pGSKI/1HeLn1ZV5qfQlPyIPVYOWGyhu4ddGtrC0YPSBqUqRSUP8MvPZt6D8GC6+Fbd+F/CVTP+YESCn5b48f5Mn9nfz7XZezdfnEDznFaJ450MlXHjvAXR+q4J9vXjmzJ+s5rOW0P/kWFNbCln/S8hpdwL2Xkin29ezjqRNP8aeTfyKajFJiL2Fb1TZuqLyBZTnLMrq3U7EY4f37Cb71NsG33iJSXw9SIqxWbGvXYr18HbbLL8e6ahU6y9lvDuFADE+LT2vEtfroPekfeTtHQFaBjbwFDvLKHOSWOshb4MCeZb4oBvMpQ38epJSE/XEGu4MMdgUZ7Aow2BVkoCtILJwYqefOt5JXpv2w+eXaU97mMo06Vry9nfChw4QPHCB84ACRhgZIJECnw7y0Bvv6K7TRgJevQ+/KrFMrlozxruddXmt7jVfbX6U31ItBZ2BTySa2V23nmrJrsBkvMDlUKgn1z8Eb/wK9RyGvRmutLf3oBf15JzxtSvLNF+r45Vut3PeRxdz3kZl7oFwK/O8X6vj33S381y0L+Yfra2bWAEkJR5+EV74JQy1QvEab2GTJtgvuvwnEArza/iovtrzInq49JGWSEnsJ15Zfy5ayLawtWHveqJ0zSQ4PE9y3j9Dedwi98w7RxkZNdqMRy/Jl2NaswbpmDZbaVRhLS0bpLOiNjrzB93cE6G/3n275A2abgdxSBznFdnJK0qXYjtU5uykclKFHi1P39ofx9oYY8oQY7gkx1BNiqCdINHTaoJttBnJK7OSWOMhdoBn2nBL7qFc2KSXxzk4iR+uI1NUROXqUyOHDJL1eAITVirW2FuuaNdguX4d17dpx49vPRUpJi6+Fvd17ebvzbfb27CWcCGPRW9hUuonryq/j6gVXX5hr5hThIXj/EXjnYRhug9zFmotmxa0X9DqeCdFEkr//z4O8cKibuzdW8o0dy9GpYe4XRCKZ4utPH+Gxfe18fN0CvnVb7cwPNksm4NDv4Y3vwvBJyFkIG/4O1nx6UiNrx2MoMsTr7a/zStsr/KXrL8RSMWwGGx8q+RAbSzZyZfGVlDnLMn6oJb1eQvv3E37vPUIHDhA5fER7ywb0OTlYVq7AsmIF1hUrsCxfjqG4eNSxo+EEA50BBjoCDHSO3Ti02I1kF9vILrSRVWQnu9CGu8CKK886I/n5LxlDHwsn8A2E8fVF8PaH8fWF8faF8PaF8Q9EOPNSrS4T2YU2sots5JTYyS7SnsI2t2nUj5r0eomeOEG08QTRxkYixxqIHjtOyu/XKuj1mBcuxLKqFmvtKqy1K7UOVENm/ryUTNE83Mz+3v3s793Pvu599IZ7ASh1lLK5dDObF2xmfdH680/blympJDS/Dgcf01rxiTBUbIINX9Ba8DNs4AEGgzG++Nv32NM8yD/euJTPX119Ubz+zgeklPzoT408+Eoj19Tk8+Ada3FbZyEHfDIOdc9o/vuOfWByaFNIrr4DKq6aliitUDzE3u697O7cze7O3fQEteCFYnsx64vWc1nBZVxWeBmVrsqM7ycZjxNpOEbkyGHCh48QOXyIaFOz5sYEdG43liVLMC9dinnxIsyLF2NetGhUw01KSXA4ymB3kKHukLbsCTLsCY2M6AUQOoEz10JWvhV3vhVXvmb83flWnLmWKfcDXBKGPpWSPPSl10klT1+P2WbAnW/FXWDDnW8lq9BGVoH2VD3Xly4TCeJdXcRaW4m1niTW2kK0qZlocxPJvtPzmutsNsw1NZiX1mCpqcGyYgXmJUvQmTPrpJRS4gl5aBhs4Ej/EQ73H+ZI/xF8MR8AuZZc1hWuY0Pxhkm3VM5LIgatu6HhBa0EerQY+BW3weWfm7FImnORUvL0gU6++Xw9/kic731sNbesLZ2Vc19q/G5vG19/+jB5DjP/669WsG1l0ew9TDveg/d+AUefgZhfC8tcukNrSFRszCjv/URIKWn1tbK3ey97u/eyv3c/g5FBALLMWazMW0ltXi0r81ayLGcZ+bbMR1inwmGix44Rrqsj2nCM6LFjRBobkaHTgReGggJMC6sxVy/EVFWFqaICU1UlxqKiUY28SCDOcG9IKx6t8entDePtC5/1FmC2GfjbH149JX1cEoYe4MibnVjsxpEn45nGXKZSJPr6SXR3aQa9s5N4Ryfx9nZi7e1aTG4yOVJf53Rirq7GVF2NeWH1yFPcUDLahzce/pifFm8LTcNNNA430jjUyLHBYwxFh7RzCB2LsxZTm1/LqrxVrCtcN32GXUpthqfmN7TWe+tuiPrAaNM6WGs/rvlSjbMXP1zf7eNbL9azu7GfNWVZfOf2WpYWTX7wjSJzDnd4uf/JQxzt8vGRZYXcf2MNiwou3J2SMbGQltX08BPaZDSJiNbAqPqwFotfdQ3kLpyWfqBThn+/Zz+H+g9xqO8QTcNNSDQbl2vJZWnOUhZlLWJR9iIWZS2iyl2VcRI/mUoR7+oieryR6IkTxJqaiLa0EGtqIhUMnq5oMGAsLcFUVo6xbAGm0lKMp0pxMfrcXET67UZKSTSUwNevGf14NMnyTVNLPX1JGHopJaF39pHo7SXR16cVj4e4x0Oip4d4by/E42fto8/KwrhgAabyMoxl5ZjKyzFVVWKqqECfkzOhwZVSMhQdoivQRWegk3Z/O+3+dtp8bbT6WukPn34TsOgtVLmrWJqzlKU5S1mWu4ya7JoL70Q9hd+jRUJ0vw/t+7RX57DWuiG7Uvtj1dyoZSU0zt4sTamU5M3GPn62u4U/n+jHYTbw37fVcOeGCvTKHz8rJJIpfvFWCz/cdZxIPMWWmnzu2VzNhxbmzq67LBbURtgee0lrfPg6tO9teVB2hTalYckaLYrHMT35jYLxIA2DDTQMNlA3UMfxoeM0DzcTS8VG6hRYC6h0V1LmLBsppc5SSuwlZJmzMrIDyf5+YidPah6Bk21a47GtjXhn50i/3SmE0YihqAhDYQHGwiIMhYUY8vMxFORjLCzEdvmYtnpCLhlDf2ztZciI1hsuLBaMhYWaEosKMRYWYSwpxlBcjLG4BGNp6VmDKc49ViAeYCA8wGBkkIHIAL2hXvrD/fSGevGEPHiCHjwhD+FE+Kx9cy25lDnLqHRXUuWuotJVyaKsRZQ6SjObpel8JGLgbdciHAaatfDH/uPQ2wDB3tP18mqgbD2UbYDKzVpOmlkkEk/yTssgL9f1sKvOg8cXpdBl5rMbK7nziorpz8miyIiBQJRH9rTxmz2t9AdilLgtXL+iiOuXF7KuMnt2J12XUst62vJGumHyjjYQ6xSOIihYCnlLtJK7UGuwuMsu2O2TSCXo8HfQNNxEi6+FFm8Lrb5WOvwdI66fU1gNVorsRRTaCim0FVJgKyDflk++NZ9cay65llxyLDnYjfZxHwjJQIB4Zyfxri7i3d0kurqI93hON0Q9ntOdwXl5LPnz7ild13RMJbgNeBBtKsGfSSm/c852kd6+HQgBd0sp92ey71hM1XUT2v8++iw3urxcYhYD4WSYUDxEMB4kGA8SiAe0Egvgj/nxx/z4Yj68US/emJfh6DDDkWGGokMkUolRxzcIA7nW3NM/vL2QUkcpxfZiSh2lLHAumFwud9A6fKJeLfolNKilGAj2QcADgV7wd4O3E3xdml9dpk7va3Zpf4L8Giiq1UrhSrDOwDD4cRgOxWjqC9LUF6Cuy8f77cPUdXmJJyVWo55ravLZtrKIG1cWz9wgHsWkiMSTvHCom51Hetjd2Ec0kcJk0LGyxMXa8myWF7uozrdTne+YnU7cU4QGwXNEezPtOQx96YZMLHC6jtCBs1jz+btKtHVHATgKwZ4PtlxttK41W/t/TLIDOBAL0O5vpyvYRVdAK6cadj2hHgbCAyRlctR+Rp2RbHM22ZZs3GY3brMbl8mFy+zCZXLhNDpxmBw4jA7sRjsOkwO7wY7VaMWqt2IOJ0j29ZEKBrGunkI2WS58cnA92uTgW4EOtMnBPyWlrDujznbgS2iGfgPwoJRyQyb7jsVUDf3WJ7bijXpHtbLHw6gz4jI5yTK5cZucuExOckxuskxOso1Ock0uck1Oco1O8oxOsvUWdDKlRRckY+llVJsrNRnT/I/xSHoZ1qJZ4mHtlXWkBCDqh4gPIl7Nb844v4HZpd3A7lJwLdCW2VVayyanSts2hVdvKSXJlCSRLrFEaqREEknCsSSReJJgLEEgmiQQSTAcjjEUjDEUitPrj+LxRuj2hvFFzggnM+pYtSCLy8qzWV+ZzaZFeViM8y951HwiFEuwu7Gf904O8X7bEIc6vEQTpxsTbquRYreFIreFfIeZbLuJbJuJLJsRu9mA02zAZtJjNemxGPVYDHpMBt1IMegERr1u6m46KbXGzmALDLVqxdsBvk6t+D1aZ++YCC0Bm8Wt/ZfMTq0YbVpEkMmmuTGNNjBYtHWDWVvXm7R1vVl7g9CbQG8iKQRDyTD98QADMT8DcR8DMR9D8QDDMR9DUS/euB9vzM9wzIc/5ieeio8j35mSCiwGC8X2Yp655Zkpqep8hj6TOJ4rgBNSyub0wR4DbgbONNY3A7+W2lNjjxAiSwhRDFRmsO+0sbmrDQNJrKkUVimxp5LYpMSeSuFMpXCkkrhkEmf6s3mGvVZRTEQxERFmwsJKBDMhYSMo7ITJwy/sBEx2AjjwCic+4WJYuBgWbgZwExNmZASIAB7tntc6lsJIWZcu2jdSQmpkXY58Tqa09WR6PZmSJNPbJ4vZoCPHbiLPYaY818aG6hwWZFtZmO+gOt9BWbZ1eifAUMw4NpOBG1YUccOKIgDiyRRtgyGa029pnUNhur0RenxhGrr9DIZixM54EGSKEKAXAr0uXYRApxPoBOiEQAiBECA49VlbH/leABQjKDmrbSOMYDZEyWWYLOnFLf1kSS9OGcBBAGcyiCMQxBoIY5cBbLIXC1GsMoxVRjATw8TEhvgUeiAvXTIlKsCn0+HX6fEJHQGdnoBOR1CnIyh0hHQ6wkIQ1oVIDQQnPuAUyMTQlwLtZ3zuQGu1T1SnNMN9ARBCfB74PED5eXKrn4+r+DAylQAEUgikXiCFDgn40eMVgk4EEkEKPVLoSKEbWWrrepLoSQkdKWFIr2vfJYWBlNCTEEaSwkASAwlhJC5M2lJnIiFMxIWJuM6MFKON3njtmlP+PQOQny6nbmhxTj1x5vq5fw6hfa8TWitBrxPo0p/1ejHyZzPodBj0AqNeYNLrMKVbYlajHotRh8Wox2424EgXt9WIVc3iM+8x6nUszHewMN/BVkanopBSEo4n8YbjBKPaG18wmiASTxKOJ4nET70dJoklU8STkkRSkkilRhoZyeTpxkYyJZFIUvJ0A+XsRov2PTDSiBmRZUQmkGlZA+kynqdirG+FTGJKxTBIrZxej2OQcfQk0MuEti4T6GQSPUltKRPoSKKTKfQkETLJKWsi5JnLpGZ5ZGrEAjkB18hniUilkMaZiYjKxNCPZZvO1dd4dTLZV/tSyoeBh0Fz3WQg1yiu/fLDU9lNoVBkiBACm8mAzXTxJvdSjCaTX6sDKDvj8wKgK8M6pgz2VSgUCsUMkolDdR+wWAhRJYQwAXcAz55T51ngLqFxJeCVUnZnuK9CoVAoZpAJW/RSyoQQ4l7gj2h9Eb+QUh4VQnwhvf2nwItoETcn0MIr//p8+87IlSgUCoViTObNgCmFQqG4lDlfeKWKhVMoFIp5jjL0CoVCMc9Rhl6hUCjmOcrQKxQKxTznouyMFUL0ASenuHse0D9hrdlHyTU5lFyTQ8k1OeajXBVSyjHzO1+Uhv5CEEK8O17P81yi5JocSq7JoeSaHJeaXMp1o1AoFPMcZegVCoVinjMfDf3FmtlMyTU5lFyTQ8k1OS4pueadj16hUCgUZzMfW/QKhUKhOANl6BUKhWKe84E39EKI7wkhGoQQh4QQTwkhxpwZWwixTQhxTAhxQghx/yzI9XEhxFEhREoIMW64lBCiVQhxWAhxQAgx45ncJiHXbOsrRwixSwjRmF5mj1NvVvQ10fWnU3L/a3r7ISHEZTMlyyTlukYI4U3r54AQ4huzINMvhBC9Qogj42yfK11NJNes6yp93jIhxGtCiPr0f/ErY9SZXp1p03d9cAtwPWBIr38X+O4YdfRAE1CNNhnKQWD5DMu1DKgBXgcuP0+9ViBvFvU1oVxzpK9/Ae5Pr98/1u84W/rK5PrR0nLvRJtF7Upg7yz8dpnIdQ3w/GzdT+lzXg1cBhwZZ/us6ypDuWZdV+nzFgOXpdedwPGZvr8+8C16KeXLUspE+uMetFmszmVkgnMpZQw4NUn5TMpVL6U8NpPnmAoZyjXr+kof/1fp9V8Bt8zw+c5HJtd/M/BrqbEHyBJCFF8Ecs06Uso3gcHzVJkLXWUi15wgpeyWUu5Pr/uBerT5tc9kWnX2gTf05/A5tKfguYw3efnFgAReFkK8l54g/WJgLvRVKLVZyUgvC8apNxv6yuT650JHmZ7zQ0KIg0KInUKIFTMsUyZczP+/OdWVEKISWAvsPWfTtOrsAzHDrxDiT0DRGJu+JqV8Jl3na0AC+O1YhxjjuwuOK81ErgzYJKXsEkIUALuEEA3plshcyjXr+prEYaZdX2OQyfXPiI4mIJNz7kfLeRIQQmwHngYWz7BcEzEXusqEOdWVEMIB/AG4T0rpO3fzGLtMWWcfCEMvpfzI+bYLIT4L7ACuk2kH1zlkMsH5tMuV4TG60steIcRTaK/nF2S4pkGuWdeXEMIjhCiWUnanX1F7xznGtOtrDDK5/hnR0YXKdabBkFK+KIT4f0KIPCnlXCbwmgtdTchc6koIYUQz8r+VUj45RpVp1dkH3nUjhNgGfBX4KykqwFymAAAB+ElEQVRlaJxqF+Uk5UIIuxDCeWodrWN5zAiBWWYu9PUs8Nn0+meBUW8es6ivTK7/WeCudHTElYD3lOtpBplQLiFEkRBCpNevQPuPD8ywXBMxF7qakLnSVfqcPwfqpZQ/HKfa9Opstnucp7ugTUjeDhxIl5+mvy8BXjyj3na03u0mNBfGTMt1K9pTOQp4gD+eKxda9MTBdDl6scg1R/rKBV4BGtPLnLnU11jXD3wB+EJ6XQA/SW8/zHkiq2ZZrnvTujmIFpywcRZkehToBuLpe+tvLhJdTSTXrOsqfd6r0Nwwh86wW9tnUmcqBYJCoVDMcz7wrhuFQqFQnB9l6BUKhWKeowy9QqFQzHOUoVcoFIp5jjL0CoVCMc9Rhl6hUCjmOcrQKxQKxTxHGXqFYgKEEOvTOcEt6dG5R4UQK+daLoUiU9SAKYUiA4QQDwAWwAp0SCm/PcciKRQZowy9QpEB6dwy+4AI2lD55ByLpFBkjHLdKBSZkQM40GYEssyxLArFpFAteoUiA4QQz6LN6FQFFEsp751jkRSKjPlA5KNXKOYSIcRdQEJK+TshhB54WwhxrZTy1bmWTaHIBNWiVygUinmO8tErFArFPEcZeoVCoZjnKEOvUCgU8xxl6BUKhWKeowy9QqFQzHOUoVcoFIp5jjL0CoVCMc/5/+F6QSP1uu/lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma_v = (0.2, 0.4, 0.6, 0.8, 1.0)\n",
    "\n",
    "pdf_list = []\n",
    "for sigma in sigma_v:\n",
    "    x = np.linspace(-2, 2, 100)\n",
    "    pdf_list.append(scipy.stats.t.pdf(x, 10, scale=sigma))\n",
    "\n",
    "#Строим график функции распределения\n",
    "plt.plot(x, pdf_list[0], label=f'$\\sigma = {sigma_v[0]}$')\n",
    "plt.plot(x, pdf_list[1], label=f'$\\sigma = {sigma_v[1]}$')\n",
    "plt.plot(x, pdf_list[2], label=f'$\\sigma = {sigma_v[2]}$')\n",
    "plt.plot(x, pdf_list[3], label=f'$\\sigma = {sigma_v[3]}$')\n",
    "plt.plot(x, pdf_list[4], label=f'$\\sigma = {sigma_v[4]}$')\n",
    "plt.xlabel('x')\n",
    "plt.title(f'Зависимость распределения Cтьюдента от $\\sigma$')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Уровень 1:\n",
    "\n",
    "### Задание 2\n",
    "\n",
    "Непрерывная случайная величина задана плотностью распределения:\n",
    "$$ f(\\xi) = C, \\xi \\in [0;5];f(\\xi) = 0, \\xi \\notin [0;5]$$\n",
    "\n",
    "Найдите C, математическое ожидание $\\xi$ а также вероятность попадания в отрезок $[3.5;7]$, т.е. P(3.5 < $\\xi$ < 7).\n",
    "\n",
    "Решение\n",
    "\n",
    "Так как С - константа, то перед нами равномерное распределение. Коэфициент равномерного распределения найдем по формуле: $1/(b-a) = 1/(5-0) = 1/5$\n",
    "\n",
    "Функция плотности распределения выглядит следующим образом:\n",
    "\n",
    "$\\begin{equation*}\n",
    " \\begin{cases}\n",
    "   1/5, \\xi \\in [0;5]\n",
    "   \\\\\n",
    "   0, \\xi \\notin [0;5]\n",
    " \\end{cases}\n",
    "\\end{equation*}$\n",
    "\n",
    "Найдем функцию распределения.\n",
    "\n",
    "Значение функции распределения для промежутка $\\xi \\in (-\\infty;0)$:  \n",
    "$\\int_{-\\infty}^{0}0dx = 0$\n",
    "\n",
    "Значение функции распределения для промежутка $\\xi \\in [0, 5]$:  \n",
    "$\\int_{-\\infty}^{0}0dx + \\int_{0}^{x}\\frac{1}{5}dx = 0 + \\frac{x}{2}|_0^x = \\frac{x}{5}$\n",
    "\n",
    "Значение функции распределения для промежутка $\\xi \\in (5,+\\infty)$:  \n",
    "$\\int_{-\\infty}^{0}0dx + \\int_{0}^{5}\\frac{1}{5}dx + \\int_{5}^{+\\infty}0dx = 0 + \\frac{x}{5}|_0^5 + 0 = 1$\n",
    "\n",
    "Итоговая функция распределения:\n",
    "\n",
    "$\\begin{equation*}\n",
    " \\begin{cases}\n",
    "   0,   \\xi \\in [-\\infty;0)\n",
    "   \\\\\n",
    "   x/5,   \\xi \\in [0;5]\n",
    "   \\\\\n",
    "   1,   \\xi \\notin (5;+\\infty)\n",
    " \\end{cases}\n",
    "\\end{equation*}$\n",
    "\n",
    "Найдем математическое ожидание.\n",
    "\n",
    "$M(x) = \\int_{-\\infty}^{0}0*xdx + \\int_{0}^{5}\\frac{1}{5}xdx + \\int_{5}^{+\\infty}0*xdx = \\frac{x^2}{10}|_0^5 = \\frac{1}{10}*(25-0) = 2.5$\n",
    "\n",
    "Найдем вероятность попадания в отрезок $[3.5;7]$ через функцию распределения:\n",
    "\n",
    "$P(3.5 < \\xi < 7) = F(7) - F(3.5) = 1 - \\frac{7}{2*5} = \\frac{3}{10} = 0.3$\n",
    "\n",
    "Найдем вероятность попадания в отрезок $[3.5;7]$ через функцию плотности распределения:\n",
    "\n",
    "$P(3.5 < \\xi < 7) = \\int_{3.5}^{5}\\frac{1}{5}dx + \\int_{5}^{+\\infty}0*dx = \\frac{x}{5}|_{3.5}^5 + 0 = \\frac{1.5}{5} = 0.3$\n",
    "\n",
    "Теперь проверим наши расчеты через python.\n",
    "\n",
    "Проверим дейсвтительно ли С = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Плотность вероятности имеет максимум в точке 0.2\n"
     ]
    }
   ],
   "source": [
    "a = 0\n",
    "b = 5\n",
    "\n",
    "uni = scipy.stats.uniform(a, b-a)\n",
    "x = np.linspace(-5, 10, 1000)\n",
    "pdf = uni.pdf(x)\n",
    "plt.plot(x, pdf)\n",
    "plt.title(\"Плотность вероятности функции из задания\")\n",
    "plt.show()\n",
    "\n",
    "if 0.2 == uni.pdf(np.random.uniform(a, b)):\n",
    "    print('Плотность вероятности имеет максимум в точке 0.2')\n",
    "else:\n",
    "    print('Максимум плотности вероятности найден неверно')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим корректность расчета математического ожидания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Математическое ожидание расчитано верно\n"
     ]
    }
   ],
   "source": [
    "if 2.5 == scipy.integrate.quad(lambda x: x*1/5, 0, 5)[0]:\n",
    "    print('Математическое ожидание расчитано верно')\n",
    "else:\n",
    "    print('Математическое ожидание расчитано неверно')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим корректность расчета вероятности попадания в отрезок $[3.5;7]$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Вероятность попадания в интервал расчитано верно\n"
     ]
    }
   ],
   "source": [
    "if 0.3 == round(scipy.integrate.quad(lambda x: 1/5, 3.5, 5)[0], 1):\n",
    "    print('Вероятность попадания в интервал расчитано верно')\n",
    "else:\n",
    "    print('Вероятность попадания в интервал расчитано неверно')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 3\n",
    "\n",
    "Известно, что светофор горит 54 секунды красным, 4 секунды жёлтым и 28 секунд зелёным. Посчитайте, какое количество информации несёт сообщение о цвете светофора в текущий момент. А что будет в случае 28, 28 и 28 секунд для каждого цвета?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Вероятность появления зеленого цвета: 0.32558139534883723\n",
      "Вероятность появления желтого цвета: 0.046511627906976744\n",
      "Вероятность появления красного цвета: 0.627906976744186\n",
      "Количество информации сообщения о светофоре в текущий момент: 1.154522162441165\n"
     ]
    }
   ],
   "source": [
    "n = 54 + 4 + 28\n",
    "p_red = 54 / n\n",
    "p_green = 28 / n\n",
    "p_yellow = 4 / n\n",
    "\n",
    "print('Вероятность появления зеленого цвета:', p_green)\n",
    "print('Вероятность появления желтого цвета:', p_yellow)\n",
    "print('Вероятность появления красного цвета:', p_red)\n",
    "\n",
    "assert p_green + p_yellow + p_red == 1, 'Вероятности цветов светофора найдены некорректно'\n",
    "\n",
    "inf = -(p_green*np.log2(p_green) + p_yellow*np.log2(p_yellow) + p_red*np.log2(p_red))\n",
    "print('Количество информации сообщения о светофоре в текущий момент:', inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество информации сообщения о светофоре в текущий момент: 1.584962500721156\n"
     ]
    }
   ],
   "source": [
    "# В случае одинаковых вероятностей появления каждого цвета можно взять формуду Шенона по количеству равновероятных событий\n",
    "n = 3\n",
    "inf = np.log2(n)\n",
    "print('Количество информации сообщения о светофоре в текущий момент:', inf)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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 },
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